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We consider the problem of communicating over a channel that randomly “tears” the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\mathrm{ Geometric}}(p_{n})$ , we characterize the capacity as $C = e^{-\alpha }$ , where $\alpha = \lim _{n\to \infty } p_{n} \log n$. Our results show that the case of ${\mathrm{ Geometric}}(p_{n})$ -length fragments and the case of deterministic length- $(1/p_{n})$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity. [ABSTRACT FROM AUTHOR]

The number-theoretic code is a class of codes defined by single or multiple congruences. These codes are mainly used for correcting insertion and deletion errors, and for correcting asymmetric errors. This paper presents a formula for a generalization of the complete weight enumerator for the number-theoretic codes. This formula allows us to derive the weight enumerators and cardinalities for the number-theoretic codes. As a special case, this paper provides the Hamming weight enumerators and cardinalities of the non-binary Tenengolts’ codes, correcting single insertion or deletion. Moreover, we show that the formula deduces the MacWilliams identity for the linear codes over the ring of integers modulo $r$. [ABSTRACT FROM AUTHOR]

Scholar name disambiguation remains a hard and unsolved problem, which brings various troubles for bibliography data analytics. Most existing methods handle name disambiguation separately that tackles one name at a time, and neglect the fact that disambiguation of one name affects the others. Further, it is typically common that only limited information is available for bibliography data, e.g., only basic paper and citation information is available in DBLP. In this study, we propose a collective approach to name disambiguation, which takes the connection of different ambiguous names into consideration. We reformulate bibliography data as a heterogeneous multipartite network, which initially treats each author reference as a unique author entity, and disambiguation results of one name propagate to the others of the network. To further deal with the sparsity problem caused by limited available information, we also introduce word-word and venue-venue similarities, and we finally measure author similarities by assembling similarities from four perspectives. Using real-life data, we experimentally demonstrate that our approach is both effective and efficient. [ABSTRACT FROM AUTHOR]

Linear codes achieving or nearly achieving the Singleton bound are interesting in both theory and practice. The objective of this paper is to construct several infinite families of MDS, near MDS and almost MDS codes from some special cyclic subgroups of ${\mathbb {F}}_{q^{2}}^{*}$. To this end, the augmentation and extension techniques are used. The codes in this paper have flexible parameters and their lengths could be large. The minimum linear locality of the codes constructed in this paper is also studied. Some infinite families of optimal linearly locally recoverable codes are obtained. Besides, some codes in this paper are proved to be proper for error detection. [ABSTRACT FROM AUTHOR]

The rapid development of the blockchain leads to a blowout of on-chain transactions, contracts, and currencies, which will further accelerate the increase of data. The existing blockchain systems typically support exact transaction queries, which, however, cannot satisfy the QoS requirements with intelligent adjustment in the blockchain systems. To this end, this paper takes the first step to define and address the practically important problem of transaction cardinality estimation for QoS-adjustable intelligent blockchain systems. We first establish a mathematical relationship between the bit string and transaction cardinality. Thus, we can leverage the number of leading 1s of the obtained bit string to estimate the transaction cardinality. We then improve the block header and body with a corresponding search algorithm to access bit strings in blocks. We also propose an estimation protocol with intelligent adjustable QoS to support accuracy-guaranteed and efficiency-optimized estimation. Finally, we design an authentication scheme and guarantee the reliability of our protocol through rigorous theoretical derivation. When achieving the transaction cardinality estimation in blockchain, two technical challenges need to be addressed. (i) To ensure efficient, verifiable, and overhead-saving bit string accessing mechanism in blockchain, we propose the Merkle Cardinality Tree (MCT) and target block filtering mechanism based on Bloom Filter (BF) in off-chain and improve on-chain block header by joining the abstract of MCT and BF. (ii) To improve estimation efficiency while guaranteeing accuracy requirements in hybrid blockchain scheme, we propose a Dynamic One-round Sampling-based cardinality Estimation (DOSE) protocol and integrate BF-DOSE to intelligently accelerate estimation. We build MCT in Ethereum and store the MCT Root in the block header for estimation authentication. Extensive experiments reveal that our BF-DOSE protocol can well satisfy various accuracy and efficiency requirements of QoS-adjustable intelligent blockchain systems, and is one to two orders of magnitude faster compared with benchmark schemes. [ABSTRACT FROM AUTHOR]

An uninterruptible power supply (UPS) can reduce power failure loss by responding to power demand during distribution network failures. For reliability-sensitive power consumers, deploying UPSs is a solution for improving power supply reliability. Nevertheless, because of UPS devices’ energy limits and potential failure probability, power consumers with UPSs cannot guarantee 100% adequacy. In future power systems with frequent reconfiguration and massive storage facilities, the power supply adequacy of a consumer with respect to the network configuration and the storage capacity will need to be frequently evaluated. Furthermore, the analytical formulation of a consumer with a UPS should be investigated since the simulation-based method might witness low efficiency and poor convergency. Therefore, this paper proposes a novel analytical availability index calculation framework for adequacy evaluation. The framework models the uncertainties of distribution networks and UPS devices as stochastic processes. Then, the availability index calculation formula is acquired based on the stochastic modeling. Compared with existing methods, this paper incorporates UPS devices’ capacity and characteristics. Moreover, the distribution network configuration and protection device placement are considered. Case studies on an illustrative distribution network, the modified RBTS system, and a real distribution system in China validate the proposed method. [ABSTRACT FROM AUTHOR]

Cycle time, which is of order of ten weeks for most products in semiconductor wafer fabrication facilities (wafer fabs), must be explicitly considered in production planning models. Cycle times depend nonlinearly on the resource workload in a wafer fab. Different data-driven (DD) production planning formulations are studied in this paper. Such formulations are based on a set of system states representing the congestion behavior of a wafer fab with work in process (WIP) and resulting output levels. Different WIP-output relations in the DD formulations are investigated. In addition, different methods are proposed to gather representative sets of system states. The DD formulations are compared with the Allocated Clearing Function (ACF) formulation with respect to profit and cost values using simulation models of a scaled-down and a large-sized multi-product wafer fab, respectively. The simulation results show that some DD variants are able to outperform the ACF formulation under several experimental conditions. [ABSTRACT FROM AUTHOR]

Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method. [ABSTRACT FROM AUTHOR]

This paper unveils that maintaining a proper voltage protection for inverter-based distributed generations (IBDGs) using overcurrent relays and during the power grids’ faults with considerable resistance stands as a complicated issue. To overcome this challenge, this paper presents a new protection scheme through developing a voltage index (VI) and launching a new characteristic that concentrates on minimizing energy-not-supplied (ENS). If the developed voltage index is in place, the near area faults with considerable resistance could be effectively discriminated from those at a far distance. Moreover, the adopted characteristic represents two operation modes known by fast and slow modes. For the faults in the vicinity of the IBDG, independent from resistances, the fast part of the proposed characteristic and VI are jointly corresponded to clear the faults swiftly. Moreover, to satisfy coordination time interval (CTI) among relays pairs, the slow part acts in the conventional manner which is corresponded during the backup process. To evaluate the performance of the proposed method, numerical studies are carried out on IEEE 33-bus test system. Results show that the ENS index is enhanced while meeting IBDGs’ voltage protection requirements and satisfying selectivity tasks. [ABSTRACT FROM AUTHOR]

This paper proposes a Principal Component Analysis (PCA)-based method to construct composite indices to rapidly assess power system transient stability. By assigning appropriate weights to a few selected sub-indicators, a composite index can be constructed to guarantee a better coverage of worst-case stability scenarios of the power system. This paper provides an automatic method to construct a composite index with four sub-indicators calculated by the integral of acceleration, speed deviation and rotor angle of generators over a certain integration period using PCA. The effectiveness of the sub-indicators is validated first, followed by a comprehensive example of process of composite index construction, and the assessment of the influence of the effect of integration period on the performance of the composite index. High accuracy of classification has been demonstrated using composite index with appropriate settings of the integration periods of the sub-indicators. [ABSTRACT FROM AUTHOR]

This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Our findings reveal a “leaky pipeline” in the field, with a low –albeit slowly increasing over the years– representation of women. They also show that academic institutions clearly dominate publications, ahead of industry and governmental centres. In terms of geography, most publications come from the USA, contributions from Latin America or Africa being almost non-existent. Lastly, we find that diversity in the characteristics of researchers (gender and geographic location) influences diversity in the topics. To conclude, we analyse initiatives that have been undertaken in other AI-related research communities to foster diversity, and recommend a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The diversity data collected in this work are publicly available, ensuring strict personal data protection and governance rules. [ABSTRACT FROM AUTHOR]

Ride-sourcing services are now reshaping the way people travel by effectively connecting drivers and passengers through mobile internets. Online matching between idle drivers and waiting passengers is one of the most key components in a ride-sourcing system. The average pickup distance or time is an important measurement of system efficiency since it affects both passengers’ waiting time and drivers’ utilization rate. It is naturally expected that a more effective bipartite matching (with smaller average pickup time) can be implemented if the platform accumulates more idle drivers and waiting passengers in the matching pool. A specific passenger request can also benefit from a delayed matching since he/she may be matched with closer idle drivers after waiting for a few seconds. Motivated by the potential benefits of delayed matching, this paper establishes a two-stage framework which incorporates a combinatorial optimization and multi-agent deep reinforcement learning methods. The multi-agent reinforcement learning methods are used to dynamically determine the delayed time for each passenger request (or the time at which each request enters the matching pool), while the combinatorial optimization conducts an optimal bipartite matching between idle drivers and waiting passengers in the matching pool. Four tailored reinforcement learning methods, delayed multi-agent deep Q learning (Delayed-M-DQN), delayed multi-agent actor-critic (Delayed-M-A2C), delayed multi-agent Proximal Policy Optimization (Delayed-M-PPO), and delayed multi-agent actor-critic with experience replay (Delayed-M-ACER), are developed. Through extensive empirical experiments with a well-designed simulator, we show that the proposed framework is able to remarkably improve system performances, by well balancing the trade-off among pick-up time, matching time, successful matching rate. [ABSTRACT FROM AUTHOR]

This paper considers restless multiarmed bandit (RMAB) problem involving partially observed Markov decision processes with heterogeneous state transition matrices in discrete time slots. Due to the notorious PSPACE-hardness of the generic RMAB, we turn to the myopic policy, i.e., scheduling the best processes each time, since the myopic policy is one of the policies with linear computation complexity and popularly adopted in practice. Intuitively, the myopic policy is not optimal because it only exploits information without exploring. In this paper, to show the optimality of the myopic policy, we first generalize the concept of the first order stochastic dominance ordering and then decompose each state transition matrix into a weighted sum of a series of eigenmatrices in which the weights are determined by the corresponding eigenvalues. Furthermore, we identify two sets of sufficient conditions on both eigenvalues and eigenmatrices to guarantee the optimality of the myopic policy for two instances, respectively. The theorectical results provide some insights into the structure of the optimal policy for the generic RMAB. [ABSTRACT FROM AUTHOR]

Many big data applications produce a massive amount of high-dimensional, real-time, and evolving streaming data. Clustering such data streams with both effectiveness and efficiency are critical for these applications. Although there are well-known data stream clustering algorithms that are based on the popular online-offline framework, these algorithms still face some major challenges. Several critical questions are still not answer satisfactorily: How to perform dimensionality reduction effectively and efficiently in the online dynamic environment? How to enable the clustering algorithm to achieve complete real-time online processing? How to make algorithm parameters learn in a self-supervised or self-adaptive manner to cope with high-speed evolving streams? In this paper, we focus on tackling these challenges by proposing a fully online data stream clustering algorithm (called ESA-Stream) that can learn parameters online dynamically in a self-adaptive manner, speedup dimensionality reduction, and cluster data streams effectively and efficiently in an online and dynamic environment. Experiments on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents improved indices to evaluate the contribution of reactive power compensators at HVdc converter terminals. The presence of such compensators impacts converter performance, such as the maximum available power (MAP) limit, susceptance to commutation failure, fault recovery time and temporary over voltage. Traditionally, the Effective Short Circuit Ratio (ESCR) has been used to indicate system strength. However, this index cannot be directly applied when power-electronics based converters are used to provide the reactive power support. The approach of this paper is to use an improved form of the ‘Apparent increase in Short Circuit Ratio’ (AISCR) index to quantify the impact of the compensator on the HVdc system. This index is evaluated by comparing the performance with the reactive power device with a system without the device, but having the same performance. The paper shows that a single AISCR index cannot quantify all behaviors and suggest modifications to AISCR for each of several HVdc phenomena. In this paper two types of compensators are considered – the Synchronous Compensator and the Static Synchronous Compensator (STATCOM). [ABSTRACT FROM AUTHOR]

This paper studies the minimum-age scheduling problem in a wireless sensor network where an access point (AP) monitors the state of an object via a set of sensors. The freshness of the sensed state, measured by the age-of-information (AoI), varies at different sensors and is not directly observable to the AP. The AP has to decide which sensor to query/sample in order to get the most updated state information of the object (i.e., the state information with the minimum AoI). In this paper, we formulate the minimum-age scheduling problem as a multi-armed bandit problem with partially observable arms and explore the greedy policy to minimize the expected AoI sampled over an infinite horizon. To analyze the performance of the greedy policy, we 1) put forth a relaxed greedy policy that decouples the sampling processes of the arms, 2) formulate the sampling process of each arm as a partially observable Markov decision process (POMDP), and 3) derive the average sampled AoI under the relaxed greedy policy as a sum of the average AoI sampled from individual arms. Numerical and simulation results validate that the relaxed greedy policy is an excellent approximation to the greedy policy in terms of the expected AoI sampled over an infinite horizon. [ABSTRACT FROM AUTHOR]

Consider a massive random access scenario in which a small set of $k$ active users out of a large number of $n$ potential users need to be scheduled in $b\ge k$ slots. What is the minimum common feedback to the users needed to ensure that scheduling is collision-free? Instead of a naive scheme of listing the indices of the $k$ active users in the order in which they should transmit, at a cost of $k\log (n)$ bits, this paper shows that for the case of $b=k$ , the rate of the minimum fixed-length common feedback code scales only as $k \log (e)$ bits, plus an additive term that scales in $n$ as $\Theta (\log \log (n))$ for fixed $k$. If a variable-length code can be used, assuming uniform activity among the users, the minimum average common feedback rate still requires $k \log (e)$ bits, but the dependence on $n$ can be reduced to $O(1)$. When $b>k$ , the number of feedback bits needed for collision-free scheduling can be significantly further reduced. Moreover, a similar scaling on the minimum feedback rate is derived for the case of scheduling $m$ users per slot, when $k \le mb$. The problem of constructing a minimum collision-free feedback scheduling code is connected to that of constructing a perfect hashing family, which allows practical feedback scheduling codes to be constructed from perfect hashing algorithms. [ABSTRACT FROM AUTHOR]

Linkages between science and technology have been extensively studied using nonpatent literature citations or author-inventor matching. These methods suffer from limitations, such as the lack of citations to relevant documents or challenges with the disambiguation of author–inventor linkages. To mitigate these limitations, this paper uses Latent Dirichlet Allocation to create topic-based linkages between publications and patents based on the semantic content in the documents. The approach allows for the detection of topical overlap between patent and scientific publications, highlighting topical areas shared by research and application. Using a case study on “Taxol,” a cancer drug, with in total 26 475 documents retrieved from EuropePMC database the study illustrates the performance of the approach. The study offers qualitative and quantitative support that the approach is valuable in detecting patent and publication linkages. [ABSTRACT FROM AUTHOR]

Signed quadrature spatial modulation (SQSM) was recently introduced to enhance the throughput of spatial modulation (SM) in multiple-input multiple-output (MIMO) systems. This strategy involves independently transmitting the real and imaginary components of a symbol and its inverse from four independent antennas. In this paper, an extended SQSM (ESQSM) system is proposed. It combines $K$ SQSM constellations and transmits them from the same transmit-antenna set with the aim of realizing a $K$ -fold improvement in the spectral efficiency (SE). The proposed ESQSM employs the average power of the $K$ SQSM constellations as an additional dimension for transmission. Performance of the proposed ESQSM surpasses that of other state-of-art SM systems such as the QSM, DSM, IQSM SQSM, and EQSM by 13 dB, 11 dB, 10.55 dB, 10 dB, and 6.8 dB, respectively. In this paper, we also propose an efficient ESQSM with a multi-user (ESQSM-MU) system inspired by the ESQSM ability in increasing the SE. The proposed ESQSM-MU system offers a significant bit error rate (BER) improvement and a higher capacity (i.e., $5K$ -users) compared to the current non orthogonal multiple access-SM (NOMA-SM) and SM-assisted multi-antenna NOMA (SM-AMA-NOMA) systems. Moreover, an efficient low-complexity detector is introduced for the ESQSM signal detection, promising a 91.2% reduction in the computational complexity of ESQSM and ESQSM-MU systems relative to the maximum likelihood (ML) with a trivial loss in performance. [ABSTRACT FROM AUTHOR]

As an important research topic in intelligent teaching systems, personalized recommendation services of learning resources can effectively solve the “information overload” problem and provide effective learning. However, the traditional learning resource recommendation technology mainly aims to improve recommendation accuracy and cannot effectively ensure the diversity and novelty of recommendation results. In this paper, the learning resource recommendation task is modelled with a multi-objective optimization problem. This paper proposes the Multi-Objective Evolutionary Algorithm-based online learning Resource Recommendation Model to balance the system’s accuracy, novelty, and diversity. The proposed model includes the following four steps: learning clustering, optimization goal setting individual representation, and genetic operator. According to the experimental results, this algorithm can improve the recommendation performance of online learning resources. Compared with the existing recommendation algorithms, more accurate, diverse, and novel learning resource recommendation results can be obtained with the proposed algorithm. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has recently received extensive research interest due to its capability to intelligently change the wireless propagation environment. For RIS-aided wireless communications in frequency division duplex (FDD) mode, channel feedback at the user equipment (UE) is essential for the base station (BS) to acquire the downlink channel state information (CSI). In this paper, a dimension reduced channel feedback scheme is proposed to reduce the channel feedback overhead by exploiting the single-structured sparsity of BS-RIS-UE cascaded channel. Since different UEs share the same sparse BS-RIS channel but have their respective RIS-UE channels, there are only limited non-zero column vectors in the BS-RIS-UE cascaded channel matrix, and different UEs share the same indexes of the non-zero columns. Thus, the downlink CSI can be decomposed into user-independent channel information (i.e., the indexes of non-zero columns) and user-specific channel information (i.e., the non-zero column vectors), where the former for all UEs can be fed back by only one UE, while the latter can be fed back with a fairly low overhead by different UEs, respectively. Simulation results show that, compared with the conventional method, the proposed scheme can reduce channel feedback overhead by more than 80% for RIS-aided wireless communications. [ABSTRACT FROM AUTHOR]

Boolean functions with high algebraic immunity are important cryptographic primitives in some stream ciphers. In this paper, two methodologies for constructing minimal binary codes from sets, Boolean functions and vectorial Boolean functions with high algebraic immunity, are proposed. More precisely, a general construction of new minimal codes using minimal codes contained in Reed-Muller codes and sets without nonzero low degree annihilators is presented. The other construction allows us to yield minimal codes from certain subcodes of Reed-Muller codes and vectorial Boolean functions with high algebraic immunity. Via these general constructions, infinite families of minimal binary linear codes of dimension $m$ and length less than or equal to $m(m+1)/2$ are obtained. Besides, a lower bound on the minimum distance of the proposed minimal linear codes is established. Conjectures and open problems are also presented. The results of this paper show that Boolean functions with high algebraic immunity have nice applications in several fields additionally to symmetric cryptography, such as coding theory and secret sharing schemes. [ABSTRACT FROM AUTHOR]

Semiconductor fabrication involves hundreds of process steps through various manufacturing tools. These processing steps are composed of many manufacturing and inspection steps. Inspection is an important step in the fabrication process to determine whether a process is in or out of control. Abrupt manufacturing or inspection tool excursion can lead to a serious low yield problem. Although commonality analysis is a proven tool for detecting abrupt tool excursion, it has gained only limited success in detecting manufacturing tool excursion outside of inspection tools. Compared with manufacturing tools, only a small number of lots or wafers pass through inspection tools. Therefore, it is difficult to construct a sufficient lot history log for inspection commonality analysis in contrast to that of manufacturing tools. Furthermore, inspection may stress a wafer during its own processing, therefore, the target wafer is changed sequentially or randomly. Accordingly, a lot history is apt to include missing traces, which hinders finding inspection tool excursion effectively. In this paper, we propose a comparative analysis framework for commonality analysis algorithms. Performance measures are suggested. To compare the performance of the algorithms effectively, we use a synthetically generated dataset in a simulation experiment. In addition, we apply the algorithms to a real problem that occurred in the fabrication process. Our proposed algorithm demonstrates superiority over the other commonality analysis algorithms in the experiments. [ABSTRACT FROM AUTHOR]

Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license. The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are one-shot auction mechanisms which allocate all the available spectrum as a unique block. In this paper, we first show how to increase the performance of those auctions (in terms of revenue, efficiency and fairness of the allocation) while preserving truthful bidding, by splitting spectrum and converting single block auctions into multi-block auctions. Simulation results illustrate how appropriately choosing the number of blocks allows to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction. [ABSTRACT FROM AUTHOR]

This paper proposes a semi-analytical method to obtain the equilibrium bidding strategies for generation and demand units in a combined oligopoly and oligopsony wholesale electricity market. Such market structure is the outcome of the increasing deployment of demand response programs that facilitate active participation of demand-side players in the price-setting process. In this analysis, the concept of supply function equilibrium (SFE) is used to investigate the oligopolistic competition among generation units. The SFE model is extended and the demand function equilibrium (DFE) is obtained to study the oligopsonistic competition among demand units. The economic behavior of a market participant is formulated as a bi-level programming (BLP) problem. The imperfect competition among generation units, as well as among demand units, are modeled as a non-cooperative game. Next, a direct method is developed to calculate all candidate equilibriums of the market, and the locational marginal prices (LMPs) in terms of the bidding strategies of the market participants. The BLP problem is solved by obtaining the coordinated Pareto-dominant Nash equilibrium of the market participants’ non-cooperative games. Finally, the proposed analysis is examined in case studies. Accordingly, we report insightful observations with respect to the impact of the changes in the new market structure, at firm-level and market-level, such as in terms of mitigating market power of generation units, the market clearing prices and quantities, surplus for generation units and demand units, and potential impact on market efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses high-resolution vehicle positioning and tracking. In recent work, it was shown that a fleet of independent but neighboring vehicles can cooperate for the task of localization by capitalizing on the existence of common surrounding reflectors, using the concept of Team Channel-SLAM. This approach exploits an initial (e.g. GPS-based) vehicle position information and allows subsequent tracking of vehicles by exploiting the shared nature of virtual transmitters associated to the reflecting surfaces. In this paper, we show that the localization can be greatly enhanced by joint sensing and mapping of reflecting surfaces. To this end, we propose a combined approach coined Team Channel-SLAM Evolution (TCSE) which exploits the intertwined relation between (i) the position of virtual transmitters, (ii) the shape of reflecting surfaces, and (iii) the paths described by the radio propagation rays, in order to achieve high-resolution vehicle localization. Overall, TCSE yields a complete picture of the trajectories followed by dominant paths together with a mapping of reflecting surfaces. While joint localization and mapping is a well researched topic within robotics using inputs such as radar and vision, this paper is first to demonstrate such an approach within mobile networking framework based on radio data. [ABSTRACT FROM AUTHOR]

This paper aims to reveal the uncertainty of traffic flow by introducing a new quantity based on the concept of information entropy (IE). We discover the existence and analyze the properties of IE of traffic flows. It is revealed by both real-world trajectory data and simulation data that the IE of traffic flows can be clearly measured and observed. More importantly, the relationships between IE and other key quantities, those are space mean speed and density, in traffic flow analysis can be described by linear and parabolic functions. We also discover that these relationships are not sensitive to traffic volume. With the inspiration from IE, another new quantity termed speed entropy (SE) is then proposed. Tests with aggregated traffic data from Performance Measurement System (PeMS) show that the pattern of the relationship between SE and flow-weighted average speed illustrates different traffic conditions. In general, a key achievement of the IE analysis is that it gives us a new pathway to better capture the intricate traffic flows from the dimension of uncertainty, thus it has the potential to enhance existing models for traffic data analysis. [ABSTRACT FROM AUTHOR]

This paper proposes using the uncertainty of information (UoI), measured by Shannon’s entropy, as a metric for information freshness. We consider a system in which a central monitor observes M binary Markov processes through m communication channels (m

Community search that finds query-dependent communities has been studied on various kinds of graphs. As one instance of community search, intimate-core group (community) search over a weighted graph is to find a connected $k$ k -core containing all query nodes with the smallest group weight. However, existing state-of-the-art methods start from the maximal $k$ k -core to refine an answer, which is practically inefficient for large networks. In this paper, we develop an efficient framework, called local exploration k-core search (LEKS), to find intimate-core groups in graphs. We propose a small-weighted spanning tree to connect query nodes, and then expand the tree level by level to a connected $k$ k -core, which is finally refined as an intimate-core group. In addition, to support the intimate group search over large weighted graphs, we develop a weighted-core index (WC-index) and two new WC-index-based algorithms for expansion and refinement phases in LEKS. Specifically, we propose a WC-index-based expansion to efficiently find a candidate graph of intimate-core group, leveraging on a two-level expansion of $k$ k -breadth and 1-depth. We propose two graph removal strategies: coarse-grained refinement is designed for large graphs to delete a batch of nodes in a few iterations; fine-grained refinement is designed for small graphs to remove nodes carefully and achieve high-quality answers. Extensive experiments on real-life networks with ground-truth communities validate the effectiveness and efficiency of our proposed methods. [ABSTRACT FROM AUTHOR]

Data outsourcing is a cost-effective solution for data owners to tackle issues such as large volumes of data, huge number of users, and intensive computation needed for data analysis. They can simply upload their databases to a cloud and let it perform all management works, including query processing. One problem with this service model is how query issuers can verify the query results they receive are indeed correct. This concern is legitimate because, as a third party, clouds may not be fully trustworthy, and as a large data center, clouds are ideal targets for hackers. There has been significant work on query result verification, but most consider only simple queries where query results can be attained by checking the raw data against the query conditions directly. In this paper, we consider the problem of enabling users to verify the correctness of the results of analytic queries. Unlike simple queries, analytic queries involve ranking functions to score a database, which makes it difficult to build data structures for verification purposes. We propose two approaches, namely one-signature and multi-signature, and show that they work well on three representative types of analytic queries, including top-k, range, and KNN queries, through both analysis and experiments. [ABSTRACT FROM AUTHOR]

With the ability to acquire and process large-scale traffic big data, a cooperative intelligent transport system can be realized. Identifying important nodes in a traffic network contributes to better traffic control, which plays a more critical role in improving the traffic efficiency of a cooperative intelligent transport system. However, existing traffic node importance evaluation methods rely on manually designed metrics such as betweenness, degree, which may lead to biased results. Meanwhile, the traditional method of iteratively deleting nodes is unsuitable for a large-scale traffic network. In this paper, we propose a novel traffic node importance evaluation method based on clustering in represented transportation network. Specifically, the proposed method first construct a length-weighted network based on the geographic road network. Then, it learns the low-dimensional embeddings of nodes employing network representation learning. Finally, it clusters each nodes with a machine learning method and identifies the critical nodes through vehicle flow of different nodes. Experimental results on the real-word dataset show that our proposed method has excellent performance compared to baseline methods. [ABSTRACT FROM AUTHOR]

Moving target defense (MTD) using distributed flexible AC transmission system (D-FACTS) devices is a promising defense strategy to detect stealthy false data injection (FDI) attacks against the power system state estimation. However, all existing studies myopically perturb the reactance of D-FACTS lines without considering the system voltage stability. In this paper, we first illustrate voltage instability induced by MTDs in a three-bus system. To address this issue, we further propose a novel MTD framework that explicitly considers system voltage stability by using continuation power flow and voltage stability indices. We mathematically derive the sensitivity matrix of voltage stability index to line impedance, on which an optimization problem for maximizing voltage stability index is formulated. This framework is tested on the IEEE 14-bus and the IEEE 118-bus transmission systems, in which net load redistribution attacks are launched by sophisticated attackers. The simulation results show the effectiveness of the proposed framework in circumventing the voltage instability while maintaining the detection effectiveness of MTD. We conduct case studies with and without the proposed framework under different MTD planning and operational methods. The impacts of the proposed two methods on attack detection effectiveness and system economic metrics are also revealed. [ABSTRACT FROM AUTHOR]

With the new feature of multi-energy coupling and the advancement of the energy market, Energy Internet (EI) has higher requirements for the efficiency and applicability of integrated energy response. This paper proposes an indirect multi-energy transaction (IMET) to promote multi-energy collaborative optimization in local energy market (LEM) and improve energy utilization through personalized responses from We-Energies (WEs). Firstly, an indirect customer-to-customer multi-energy transaction is modeled for local multi-energy coupling market which can satisfy privacy, preference and autonomy of users. The efficiency of energy matching can be promoted through the participation of conversion devices. In addition, multi-time scale hybrid trading mechanism is constructed with the consideration of the transmission speed of different energy sources. Meanwhile, energy transaction process is built as a Markov decision process (MDP) with deep reinforcement learning algorithm so that the system modeling error can be successfully avoided. Furthermore, a distributed training structure is utilized to obtain more experience for a wider range of scenarios. The results of numerical simulations demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Some of the deadliest wildfires in the U.S., such as California’s 2018 wildfires, have been ignited by power systems. In an effort to prevent and minimize the ignition of wildfires, or control them if ignited, energy companies have developed wildfire mitigation plans. This paper provides energy companies and power system operators, engineers, researchers, and suppliers an overview of the state-of-the-art studies that address key topics in these wildfire mitigation plans and compares the wildfire mitigation plans of several energy companies. The key topics include grid design and system hardening, asset management and inspection, situational awareness and forecasting, operational response, vegetation management, public safety power shutoff, and risk-spend efficiency. This paper also presents a comparison of several energy companies’ decision-making criteria for initiating a public safety power shutoff. Finally, we discuss opportunities for future research studies that could help energy companies prevent wildfire ignitions. [ABSTRACT FROM AUTHOR]

It is possible to launch undetectable load-redistribution (LR) attacks against power systems even in systems with protection schemes. Therefore, it is imperative to detect LR attacks in power systems and establish corrective actions to provide secure dispatch points. This paper develops a systematic real-time LR threat analysis (RTLRTA) tool to flag LR attacks and identify all affected transmission assets. Since attackers might introduce some forms of random strategies to create LR attacks, we introduce different optimization models to generate random LR attacks to determine accurate thresholds for our detection index and test the tool’s functionality. Additionally, based on an estimation for the actual loads in the post-attack stage, we design a set of physical line flow security constraints (PLFSCs) that can be added to the existing security-constrained economic dispatch (SCED) models. This leads to appropriate responses to the identified LR attacks and provides secure dispatch points. We generate multiple scenarios of LR attacks and random errors for different target lines in the 2383-bus Polish test system to validate our proposed methods’ accuracy and functionality in detecting LR attacks and responding to them. [ABSTRACT FROM AUTHOR]

In contrast to their seemingly simple and shared structure of independence and stationarity, Lévy processes exhibit a wide variety of behaviors, from the self-similar Wiener process to piecewise-constant compound Poisson processes. Inspired by the recent paper of Ghourchian, Amini, and Gohari, we characterize their compressibility by studying the entropy of their double discretization (both in time and amplitude) in the regime of vanishing discretization steps. For a Lévy process with absolutely continuous marginals, this reduces to understanding the asymptotics of the differential entropy of its marginals at small times, for which we obtain a new local central limit theorem. We generalize known results for stable processes to the non-stable case, with a special focus on Lévy processes that are locally self-similar, and conceptualize a new compressibility hierarchy of Lévy processes, captured by their Blumenthal–Getoor index. [ABSTRACT FROM AUTHOR]

Visual exploration has become an integral part of big spatial data management. With the increase in volume and number of spatial datasets, several specialized mechanisms have been proposed to speed up the exploration of these datasets. However, the existing techniques have major limitations which make them incapable of providing visual exploration for hundreds of thousands of big datasets on a single machine. This paper introduces a new index structure, termed AID*, that facilitates the visual exploration of an arbitrarily large number of big spatial datasets on a single machine. The AID* index defines multi-resolution fixed-size tiles on the input and classifies them as image, data, shallow, or empty tiles, based on their processing cost. Then, it uses this classification to build an index with a minimal index size and construction time, while supporting the desired real-time exploration interface. The index is constructed in parallel, using Hadoop or Spark, and is accessible to end users through a standard web interface similar to Google Maps. The small size of the index allows a single-machine server to host arbitrarily many datasets. Our experiments, on up-to 1 TB of data and 27 billion records, show that the construction of the proposed index is up-to an order of magnitude faster than the baselines without compromising the end-user interactivity. [ABSTRACT FROM AUTHOR]

Mal-operation of distance protection relays during power swing (PS) condition can endanger the power system stability. Therefore, it is crucial to distinguish between short-circuit faults and PS by a power swing blocking (PSB) function to prevent undesired tripping of distance relay. This paper develops a novel and straightforward PSB method based on the rate of impedance angle change (RIAC). Not only can the proposed approach fairly discriminate both symmetrical and asymmetrical faults from PS, but also it can appropriately preserve protection scheme security during PS under single-pole tripping (SPT) on a transmission line. Furthermore, there is no need to perform extensive dynamic stability studies for setting the PSB function parameters. Unlike the rate of impedance change approaches utilized two blinders, the developed approach only needs an inner blinder. Thus, it can be used for networks running near their operational limits. The proposed method is evaluated through a modified IEEE-PSRC benchmark system in PSCAD/EMTC environment. The obtained results verify superior performance of the proposed method to traditional impedance rate of change method under different scenarios. [ABSTRACT FROM AUTHOR]

The optimal allocation of protective devices and switches in distribution systems concerns the analysis of conflicting attributes, such as capital investment, SAIDI, ENS, ECOST, MAIFI, among others. Regarding this multiattribute context, this paper proposes a multicriteria strategy to support decision-making. From this strategy, an assertive decision can be made under different scenarios when defining an appropriate solution to a real-world problem. A multiobjective optimization approach is applied to approximate an adequate set of potential solutions for allocating protective devices and switches. The capital investment and the SAIDI index are assumed as objective functions of the problem, and the well-known NSGA-II method is employed as the optimizer. Experiments are performed using the RBTS - Bus 4; the obtained trade-off solutions are evaluated in the previously stated attributes, and assisted decisions are made to illustrate the effect of considering different scenarios of preferences from the decision unit. The results highlight both the advantages of a multiobjective approach for allocating protective devices and the usefulness of a multicriteria strategy to aid assertive decision-making under different scenarios. [ABSTRACT FROM AUTHOR]

Future distribution grids are expected to face an increasing penetration of electric vehicles (EVs) and heterogeneous distributed energy resources (DERs). This demands a distributed energy management (EM) to manage power generation and delivery of energy sources to maintain power quality under the impact of EV charging, to save operating costs, and to enhance resiliency. However, the global optimality of the distributed EM’s optimization problem is still an issue in existing works because of the non-convex nature of the optimization problem. In this paper, a distributed EM strategy for grid-connected distribution networks is proposed. In particular, the EM strategy is composed of two steps. In the first step, some conditions of the EM optimization task are relaxed to apply an algorithm converging to the global optimality. The results of the first step are used to reconfigure constraints of the full optimization problem in Step 2. The proposed scheme is validated by implementing the real-time controller-hardware-in-the-loop (CHIL) experimentation on the IEEE 33 bus system. To study the impact of EV charging, EV data is collected from the market and the literature to generate realistic EV load profiles to demonstrate the effectiveness of the proposed strategy on saving operating costs and maintaining power quality. [ABSTRACT FROM AUTHOR]

Car-following models describe how a driver follows the leading vehicle in the same lane. They serve as the cornerstone of microscopic traffic-flow simulations and play an essential role in analyzing human factors in traffic casualty, congestion, efficiency, and emissions. An extensive and continuously growing number of car-following models in the literature raises the requirement to evaluate and compare different models objectively. Generally, a car-following model is evaluated after model parameter calibration: the optimal residual between the calibrated model output and the measured counterpart is used as a metric to assess a car-following model’s performance. However, model parameter calibration, usually formed as a numerical optimization problem, suffers from several issues, such as local optimality and heavy computational burden. More importantly, different formulations of the cost function can lead to distinct calibration outcomes and contradictory conclusions of the model evaluation results. This paper proposes instead a purely algebraic framework for evaluating a class of car-following models whose parameters can be linearly identified. Car-following models with nonlinear relationships among parameters, e.g., the behavioral car-following models, are out of the scope of analysis in this paper. Algebraic manipulations performed on a model finally produce a system error index, which is a uniform metric for evaluating and comparing different car-following models. During the whole process, no cost function needs to be designed a priori, and no computationally expensive numerical optimization is involved. Three car-following models are evaluated and compared under the proposed algebraic framework. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven study on locating parking hubs in free-floating ride share systems. Recently, there has been an increase in free-floating ride share systems, where users are allowed to pick up and drop off shared vehicles anywhere in the service area. However, these systems can suffer from significant demand and supply imbalance, while certain parking habits may disturb the desired city layout. A potential solution is to allocate parking hubs in an optimal manner to regulate the behaviour of the users. This paper develops a scenario optimization model for finding the optimal locations of parking hubs. The model determines the capacities and locations of the parking hubs, while considering the uncertainty of parking demand and points of interest in the area. We design an algorithm that combines the idea of Constraint-and-Column Generation and the Alternating Direction Method of Multipliers (ADMM) algorithm to solve the optimization problem in a decentralized manner, and accompany the computed solution with a probabilistic performance certificate. We also compare the adopted approach with respect to a worst case paradigm both in terms of computational cost and in terms of conservatism of the resulting solution. Numerical results show that the proposed method leads to a less conservative performance compared to the worst case method, and reduces the computational cost compared to the classical ADMM. [ABSTRACT FROM AUTHOR]

As a challenging attempt to replace a traditional data structure with a learned model, this paper proposes a learned functional Bloom filter (L-FBF) for a key–value storage. The learned model in the proposed L-FBF learns the characteristics and the distribution of given data and classifies each input. It is shown through theoretical analysis that the L-FBF provides a lower search failure rate than a single FBF in the same memory size, while providing the same semantic guarantees. For model training, character-level neural networks are used with pretrained embeddings. In experiments, four types of different character-level neural networks are trained: a single gated recurrent unit (GRU), two GRUs, a single long short-term memory (LSTM), and a single one-dimensional convolutional neural network (1D-CNN). Experimental results prove the validity of theoretical results, and show that the L-FBF reduces the search failures by 82.8% to 83.9% when compared with a single FBF under the same amount of memory used. [ABSTRACT FROM AUTHOR]

A technique for trading off the main lobe width against sidelobe magnitude for any arbitrary window was reported in Lim et al. and subsequently, a fast convergence method for its implementation was proposed by the same authors. These methods require the computation of derivatives involving the evaluation of trigonometric and hyperbolic functions. In this paper, we show that the derivatives can be computed without evaluating trigonometric and hyperbolic functions if the window function is a sum of exponentials such as a Fourier series. [ABSTRACT FROM AUTHOR]

In this paper, we propose a precoded faster-than-Nyquist (FTN) signaling technique for time-domain single-carrier index-modulated symbol transmission. More precisely, eigenvalue decomposition (EVD) precoding is adopted for the FTN transmission of data bits modulated by single-carrier time-domain index modulation (IM). While the FTN scheme increases the spectral efficiency and data rate by increasing the density of transmit symbols, the time-domain IM works towards the same objective while maintaining symbol sparsity. We analytically derive the constrained capacity of the proposed system. Our simulation results show that the proposed scheme has better bit error ratio (BER) performance than the conventional FTN-IM scheme, particularly for the scenario of a higher packing ratio. With the proposed scheme, a 2.5 dB performance gain is observed at the BER of 10−4, where the packing ratio is 0.7 and the roll-off factor is 0.5 in the channel-uncoded scenario. We further analyze our scheme’s performance by characterizing its minimum Euclidean distance and inter-symbol interference. Furthermore, we present a three-stage serially concatenated iterative detection architecture for the proposed EVD-FTN-IM signaling scheme. It is demonstrated in our simulations that a near-capacity BER performance is achievable in the proposed turbo-coded FTN-IM scheme. [ABSTRACT FROM AUTHOR]

This paper describes new, simple, recursive methods of construction for orientable sequences, i.e. periodic binary sequences in which any $n$ -tuple occurs at most once in a period in either direction. As has been previously described, such sequences have potential applications in automatic position-location systems, where the sequence is encoded onto a surface and a reader needs only examine $n$ consecutive encoded bits to determine its location and orientation on the surface. The only previously described method of construction (due to Dai et al.) is somewhat complex, whereas the new techniques are simple to both describe and implement. The methods of construction cover both the standard ‘infinite periodic’ case, and also the aperiodic, finite sequence, case. Both the new methods build on the Lempel homomorphism, first introduced as a means of recursively generating de Bruijn sequences. [ABSTRACT FROM AUTHOR]

Tang and Ding [IEEE IT 67 (2021) 244-254] studied the class of BCH codes $\mathcal {C}_{(q,q+1,4,1)}$ and their dual codes with $q=2^{m}$ and established that the codewords of the minimum (or the second minimum) weight in these codes support 4-designs or 3-designs. Motivated by this, we further investigate the codewords of the next adjacent weight in such codes and discover more infinite classes of $t$ -designs with $t=3,4$. In particular, we prove that codewords of weight 7 in $\mathcal {C}_{(q,q+1,4,1)}$ support 4-designs for odd $m \geqslant 5$ and they support 3-designs for even $m \geqslant 4$ , which provide infinite classes of simple $t$ -designs with new parameters. Another significant class of $t$ -designs we produce in this paper has complementary designs with parameters 4- $(2^{2s+1}+ 1,5,5)$ ; these designs have the smallest index among all the known simple 4- $(q+1,5,\lambda)$ designs derived from codes for prime powers $q$ ; and they are further proved to be isomorphic to the 4-designs admitting the projective general linear group PGL $(2,2^{2s+1})$ as the automorphism group constructed by Alltop in 1969. [ABSTRACT FROM AUTHOR]

Night time vehicle detection and tracking using traditional visible light cameras is a challenging task due to the limited visibility. The current state-of-the-art systems treat the vehicles at night time as paired vehicle headlights or taillights, with no ability to determine the contour of the vehicle or its spatial occupancy. Therefore, this paper proposes the first night time framework that combines the vehicle headlights and taillights to localize the vehicle contours. This new framework includes a novel multi-camera vehicle representation that groups and reconstructs vehicle headlights and taillights following mutual geometric distances between different vehicle components. This novel vehicle contour representation successfully removes duplicated vehicle lights and also compensates for the missing vehicle lights in the detection process. Eventually, vehicle headlight alignment and contour adjustment are used to further refine the vehicle contours. The proposed multi-camera system considers typical four-wheel vehicles, e.g., cars and SUVs, in the monitoring and might not be able to handle large trucks, e.g., 18-wheelers. The experiments are conducted on night time traffic videos under various scenarios and the proposed system attains an average of 0.896 in Multiple Object Tracking Accuracy (MOTA) and an average of 0.904 in Jaccard Coefficient (JC), which indicates 19.2% and 15.9% increases over the baseline system. The night time traffic datasets used in this paper are available at https://github.com/JustMeZXX/Intelligent-Night-Time-Traffic-Surveillance [ABSTRACT FROM AUTHOR]

Block orders are increasingly adopted in many electricity markets as they enable power producers to implicitly express their unit commitment characteristics and address price volatility risks. However, previous work on the optimal offering problem of a price-taking producer participating in such markets has only considered the simplest type of block orders (regular block orders-RBO) and has neglected relevant realistic market regulations. This paper addresses these simplifications by proposing a new optimal offering model which also considers the more complex profile block orders (PBO) and linked block orders (LBO). This model is generic in that it can treat the block orders’ time spans either as fixed parameters (following the modelling approach of previous work) or as endogenous decision variables, which constitutes a completely novel modeling approach. The presented case studies demonstrate that this novel approach yields a tremendously lower number of binary variables and more efficient computational performance, when PBO and LBO are considered. Furthermore, the case studies analyze the physical conditions under which the introduction of different types of block orders increases the examined producer’s profit. [ABSTRACT FROM AUTHOR]